Software-Defined Networking (SDN) has emerged as a paradigm-shifting approach to network management, offering flexibility and control. In SDN, network switches are centrally controlled by one or more controllers, enabling dynamic configuration and management. This project focuses on an intelligent SDN switch migration system for load balancing. The proposed solution leverages real-time monitoring to identify overloaded and underutilized controllers. Switches are then migrated from heavily loaded to less burdened controllers, ensuring efficient resource utilization and mitigating network congestion. The system incorporates sophisticated migration algorithms that consider factors like traffic patterns. The project has been further enhanced by integrating machine learning (ML) and neural network (NN) models, such as Ensemble Methods, KNN Regressor, Elastic Net Regressor, and Temporal Convolutional Networks (TCNs). These models utilize data from the master controller to predict future load on switches, enabling proactive switch migration decisions for optimal resource allocation and congestion mitigation.